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WPS -> words/s
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@ -153,7 +153,7 @@ def print_mean_with_ci(sample: numpy.ndarray):
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low = bootstrap_means[int(len(bootstrap_means) * 0.025)]
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high = bootstrap_means[int(len(bootstrap_means) * 0.975)]
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print(f"Mean: {mean:.1f} WPS (95% CI: {low-mean:.1f} +{high-mean:.1f})")
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print(f"Mean: {mean:.1f} words/s (95% CI: {low-mean:.1f} +{high-mean:.1f})")
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def print_outliers(sample: numpy.ndarray):
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@ -1194,7 +1194,7 @@ $ python -m spacy benchmark speed [model] [data_path] [--batch_size] [--no-shuff
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| `--batches` | Number of batches to benchmark on. Defaults to `50`. ~~Optional[int] \(option)~~ |
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| `--warmup`, `-w` | Iterations over the benchmark data for warmup. Defaults to `3` ~~Optional[int] \(option)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| **PRINTS** | Pipeline speed with 95% confidence interval. |
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| **PRINTS** | Pipeline speed in words per second with a 95% confidence interval. |
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## apply {id="apply", version="3.5", tag="command"}
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